Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	104,533
2	South Dakota	91,461
3	Iowa	73,869
4	Wisconsin	71,510
5	Nebraska	67,304
6	Utah	61,841
7	Montana	59,248
8	Illinois	58,437
9	Wyoming	58,409
10	Idaho	57,995
11	Minnesota	57,161
12	Kansas	55,166
13	Rhode Island	54,661
14	Tennessee	54,469
15	Arkansas	52,789
16	Mississippi	51,882
17	Missouri	51,797
18	Alabama	51,578
19	Indiana	51,557
20	Louisiana	51,140
21	Nevada	50,423
22	Oklahoma	50,412
23	New Mexico	47,413
24	Florida	46,939
25	Arizona	46,455
26	Alaska	44,988
27	Texas	43,988
28	Georgia	43,229
29	South Carolina	42,517
30	Kentucky	41,920
31	Colorado	41,463
32	Michigan	39,535
33	New Jersey	38,699
34	Delaware	37,322
35	Ohio	36,794
36	North Carolina	35,146
37	New York	33,929
38	Maryland	33,326
39	Connecticut	33,308
40	Massachusetts	33,254
41	California	31,733
42	District of Columbia	30,726
43	Pennsylvania	29,111
44	Virginia	28,125
45	West Virginia	27,239
46	Puerto Rico	27,220
47	Washington	22,977
48	Oregon	18,174
49	New Hampshire	16,007
50	Hawaii	12,747
51	Maine	8,909
52	Vermont	6,793

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Rhode Island	1,243
2	Minnesota	1,080
3	Wyoming	1,080
4	New Mexico	864
5	Nebraska	839
6	Tennessee	785
7	Montana	773
8	Indiana	761
9	North Dakota	752
10	Alaska	743
11	Kentucky	733
12	Colorado	706
13	Arizona	703
14	Illinois	695
15	South Dakota	683
16	Idaho	682
17	Ohio	667
18	Wisconsin	650
19	Utah	641
20	Kansas	623
21	Iowa	621
22	Missouri	617
23	Nevada	616
24	Michigan	586
25	Connecticut	577
26	Delaware	572
27	West Virginia	552
28	Alabama	537
29	Louisiana	511
30	Mississippi	500
31	Oklahoma	476
32	Arkansas	473
33	Texas	469
34	New Jersey	436
35	New Hampshire	432
36	Pennsylvania	428
37	California	401
38	Maryland	379
39	Washington	360
40	New York	357
41	Florida	354
42	Oregon	327
43	Massachusetts	325
44	North Carolina	316
45	Georgia	262
46	South Carolina	259
47	Virginia	251
48	Puerto Rico	181
49	District of Columbia	178
50	Maine	170
51	Vermont	110
52	Hawaii	41

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,923
2	New York	1,759
3	Massachusetts	1,563
4	Connecticut	1,413
5	Louisiana	1,388
6	Rhode Island	1,302
7	Mississippi	1,288
8	North Dakota	1,259
9	South Dakota	1,071
10	Illinois	1,039
11	Michigan	977
12	District of Columbia	970
13	Arizona	918
14	Georgia	871
15	Indiana	871
16	Florida	869
17	South Carolina	855
18	Arkansas	832
19	Pennsylvania	829
20	Delaware	797
21	Iowa	776
22	Maryland	772
23	Texas	765
24	New Mexico	757
25	Alabama	741
26	Nevada	708
27	Missouri	672
28	Tennessee	671
29	Montana	654
30	Minnesota	651
31	Wisconsin	623
32	Nebraska	561
33	Ohio	560
34	Colorado	544
35	Idaho	540
36	Kansas	535
37	North Carolina	507
38	California	489
39	Virginia	479
40	Kentucky	469
41	Oklahoma	444
42	West Virginia	422
43	Wyoming	397
44	New Hampshire	388
45	Washington	384
46	Puerto Rico	351
47	Utah	277
48	Oregon	222
49	Hawaii	170
50	Maine	159
51	Alaska	158
52	Vermont	115

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	North Dakota	17
2	Nebraska	11
3	Rhode Island	10
4	Indiana	9
5	Michigan	9
6	Missouri	9
7	Montana	9
8	New Mexico	9
9	Idaho	8
10	Illinois	8
11	Wisconsin	8
12	Wyoming	8
13	Colorado	7
14	Connecticut	7
15	Iowa	7
16	Pennsylvania	7
17	West Virginia	7
18	Arkansas	6
19	Mississippi	6
20	Nevada	6
21	Kentucky	5
22	Maine	5
23	Minnesota	5
24	Alabama	4
25	Delaware	4
26	Louisiana	4
27	Massachusetts	4
28	New Jersey	4
29	Ohio	4
30	Puerto Rico	4
31	Tennessee	4
32	Texas	4
33	Washington	4
34	District of Columbia	3
35	Florida	3
36	Kansas	3
37	Maryland	3
38	Oklahoma	3
39	Oregon	3
40	South Carolina	3
41	Arizona	2
42	Georgia	2
43	New York	2
44	North Carolina	2
45	South Dakota	2
46	Utah	2
47	Vermont	2
48	California	1
49	New Hampshire	1
50	Virginia	1
51	Alaska	0
52	Hawaii	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	204,752	1	99
Norton	Kansas	203,693	2	99
Bon Homme	South Dakota	197,942	3	99
Buffalo	South Dakota	192,661	4	99
Lincoln	Arkansas	190,187	5	99
Davidson	Tennessee	67,940	502	84
Richland	South Carolina	49,392	1230	60
York	South Carolina	35,326	2040	35
Orange	California	25,705	2528	19
Pierce	Washington	20,476	2732	13

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,587	1	99
Jerauld	South Dakota	6,458	2	99
Dickey	North Dakota	5,952	3	99
Hancock	Georgia	5,439	4	99
Emporia city	Virginia	5,238	5	99
Richland	South Carolina	726	1297	58
Davidson	Tennessee	609	1547	50
Orange	California	497	1823	41
York	South Carolina	488	1852	41
Pierce	Washington	355	2219	29

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.